1. Technical Field
The present invention pertains to the field of elevator control. More particularly, the present invention pertains to adding input nodes to a neural network used as part of an elevator dispatching system in response to observing use patterns not adequately encoded by the existing network input nodes.
2. Description of Related Art
Elevator dispatching systems use a number of factors in determining which elevator car is the most appropriate to service a request, called a hall call, issued by someone on a floor in the building serviced by the elevator. An elevator dispatching system often uses as an input a so called remaining response time (RRT) in deciding whether to assign an elevator to service a hall call. The remaining response time may be defined as the estimated time for the elevator to travel from its current position to the floor of the hall call.
Artificial neural networks have recently been applied to the problem of estimating RRT. See, e.g., U.S. Pat. No. 5,672,853 to Whitehall et al. Neural networks have proven useful in estimating RRT, but in implementations so far the architecture of the a neural network has been decided before the neural network is put to use, and not changed to accommodate changing patterns of use of the elevator. The architecture of a neural network encompasses what layers are used, the nodes for each layer, and the connections between the nodes. The connection weights, which express how important the output of a first node is for another node to which the first node is connected, are not intended to be encompassed by the term architecture as it is used here.
Usually, the architecture, and in particular the number of input nodes, is determined before the neural network is ever put into service with the elevator. Then the neural network is trained with some training data that reflects what is known about the use of the elevator at the time of training. By training is meant the application of a learning rule, or learning algorithm, that adjusts the weights to provide that each neural network output corresponds properly to values provided to the input nodes.
According to the prior art, once a neural network is put into operation with an elevator, its architecture is static. In other words, if the building population changes or traffic patterns change, the predetermined inputs may not adequately sort out all the factors on which remaining response time could reasonably depend; then the neural network estimate of remaining response time may not be adequate.
For example, one particular floor of a building may differ significantly from the other floors in its need for elevator service. Normally, inputs to a neural network used to estimate remaining response time in an elevator dispatching system are not specialized to particular floors at the outset, unless the special use is anticipated. Thus, after a neural network is put into operation with an elevator, use information collected by the elevator dispatching system may suggest that a particular floor unexpectedly stands out from the other floors in its need for elevator service. Although the neural network weights can be adjusted during actual operation of the elevator, as disclosed in U.S. Patent Application "Method For Continuous Learning By A Neural Network Used In An Elevator Dispatching System" by Whitehall et al., filed on even date herewith, such adjustment may not adequately account for the special use. The existing inputs may simply not be adequate for the neural network to sort out all of the dependencies that should be included in making a good estimate of remaining response time.
What is needed is a way of implementing a neural network so that it can adapt continuously to observed special use patterns that are not adequately represented by existing inputs.